Overview

Dataset statistics

Number of variables40
Number of observations28836
Missing cells25774
Missing cells (%)2.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 MiB
Average record size in memory178.1 B

Variable types

Numeric15
Categorical25

Alerts

IncidentAddress has a high cardinality: 1000 distinct valuesHigh cardinality
InsuredAge is highly overall correlated with CustomerLoyaltyPeriodHigh correlation
InsuredZipCode is highly overall correlated with InsuredGender and 22 other fieldsHigh correlation
CustomerLoyaltyPeriod is highly overall correlated with InsuredAgeHigh correlation
AmountOfTotalClaim is highly overall correlated with AmountOfInjuryClaim and 3 other fieldsHigh correlation
AmountOfInjuryClaim is highly overall correlated with AmountOfTotalClaim and 3 other fieldsHigh correlation
AmountOfPropertyClaim is highly overall correlated with AmountOfTotalClaim and 3 other fieldsHigh correlation
AmountOfVehicleDamage is highly overall correlated with AmountOfTotalClaim and 3 other fieldsHigh correlation
InsuredGender is highly overall correlated with InsuredZipCodeHigh correlation
InsuredEducationLevel is highly overall correlated with InsuredZipCodeHigh correlation
InsuredOccupation is highly overall correlated with InsuredZipCodeHigh correlation
InsuredHobbies is highly overall correlated with InsuredZipCodeHigh correlation
InsurancePolicyState is highly overall correlated with InsuredZipCodeHigh correlation
InsuredRelationship is highly overall correlated with InsuredZipCodeHigh correlation
TypeOfIncident is highly overall correlated with InsuredZipCode and 5 other fieldsHigh correlation
TypeOfCollission is highly overall correlated with InsuredZipCodeHigh correlation
SeverityOfIncident is highly overall correlated with InsuredZipCodeHigh correlation
AuthoritiesContacted is highly overall correlated with InsuredZipCodeHigh correlation
IncidentState is highly overall correlated with InsuredZipCodeHigh correlation
IncidentCity is highly overall correlated with InsuredZipCodeHigh correlation
NumberOfVehicles is highly overall correlated with InsuredZipCode and 1 other fieldsHigh correlation
PropertyDamage is highly overall correlated with InsuredZipCodeHigh correlation
BodilyInjuries is highly overall correlated with InsuredZipCodeHigh correlation
Witnesses is highly overall correlated with InsuredZipCodeHigh correlation
PoliceReport is highly overall correlated with InsuredZipCodeHigh correlation
VehicleMake is highly overall correlated with InsuredZipCode and 1 other fieldsHigh correlation
VehicleModel is highly overall correlated with InsuredZipCode and 1 other fieldsHigh correlation
ReportedFraud is highly overall correlated with InsuredZipCodeHigh correlation
SplitLimit is highly overall correlated with InsuredZipCode and 1 other fieldsHigh correlation
CombinedSingleLimit is highly overall correlated with InsuredZipCode and 1 other fieldsHigh correlation
MonthOfIncident is highly overall correlated with InsuredZipCodeHigh correlation
TypeOfCollission has 5162 (17.9%) missing valuesMissing
PropertyDamage has 10459 (36.3%) missing valuesMissing
PoliceReport has 9805 (34.0%) missing valuesMissing
CapitalGains has 15819 (54.9%) zerosZeros
CapitalLoss has 14759 (51.2%) zerosZeros
UmbrellaLimit has 21073 (73.1%) zerosZeros
IncidentTime has 391 (1.4%) zerosZeros

Reproduction

Analysis started2023-03-16 00:46:47.357440
Analysis finished2023-03-16 00:47:34.024113
Duration46.67 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

InsuredAge
Real number (ℝ)

Distinct46
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.81537
Minimum19
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:34.152260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile27
Q133
median38
Q344
95-th percentile54
Maximum64
Range45
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.9963773
Coefficient of variation (CV)0.20601059
Kurtosis-0.089283925
Mean38.81537
Median Absolute Deviation (MAD)5
Skewness0.50641259
Sum1119280
Variance63.942051
MonotonicityNot monotonic
2023-03-16T06:17:34.290244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
34 1469
 
5.1%
38 1419
 
4.9%
37 1401
 
4.9%
39 1365
 
4.7%
36 1311
 
4.5%
40 1307
 
4.5%
35 1302
 
4.5%
33 1295
 
4.5%
32 1262
 
4.4%
41 1244
 
4.3%
Other values (36) 15461
53.6%
ValueCountFrequency (%)
19 1
 
< 0.1%
20 12
 
< 0.1%
21 33
 
0.1%
22 40
 
0.1%
23 78
 
0.3%
24 149
 
0.5%
25 248
 
0.9%
26 421
1.5%
27 601
2.1%
28 804
2.8%
ValueCountFrequency (%)
64 10
 
< 0.1%
63 13
 
< 0.1%
62 35
 
0.1%
61 118
0.4%
60 148
0.5%
59 164
0.6%
58 197
0.7%
57 190
0.7%
56 242
0.8%
55 261
0.9%

InsuredZipCode
Real number (ℝ)

Distinct995
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean502436.58
Minimum430104
Maximum620962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:34.423479image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum430104
5-th percentile433184
Q1448603
median466691
Q3603848
95-th percentile617721
Maximum620962
Range190858
Interquartile range (IQR)155245

Descriptive statistics

Standard deviation72250.869
Coefficient of variation (CV)0.14380097
Kurtosis-1.2586207
Mean502436.58
Median Absolute Deviation (MAD)22536
Skewness0.77530589
Sum1.4488261 × 1010
Variance5.2201881 × 109
MonotonicityNot monotonic
2023-03-16T06:17:34.576760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
446895 100
 
0.3%
612904 72
 
0.2%
476198 71
 
0.2%
608331 70
 
0.2%
440961 70
 
0.2%
477695 69
 
0.2%
457555 68
 
0.2%
432711 66
 
0.2%
433184 66
 
0.2%
478456 62
 
0.2%
Other values (985) 28122
97.5%
ValueCountFrequency (%)
430104 26
0.1%
430141 43
0.1%
430232 23
0.1%
430380 35
0.1%
430567 40
0.1%
430621 37
0.1%
430632 22
0.1%
430665 26
0.1%
430714 16
 
0.1%
430832 21
0.1%
ValueCountFrequency (%)
620962 31
0.1%
620869 19
0.1%
620819 29
0.1%
620757 36
0.1%
620737 27
0.1%
620507 26
0.1%
620493 30
0.1%
620473 19
0.1%
620358 22
0.1%
620207 25
0.1%

InsuredGender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing30
Missing (%)0.1%
Memory size253.6 KiB
FEMALE
15644 
MALE
13162 

Length

Max length6
Median length6
Mean length5.0861626
Min length4

Characters and Unicode

Total characters146512
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALE
2nd rowMALE
3rd rowMALE
4th rowMALE
5th rowFEMALE

Common Values

ValueCountFrequency (%)
FEMALE 15644
54.3%
MALE 13162
45.6%
(Missing) 30
 
0.1%

Length

2023-03-16T06:17:34.736454image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:34.876501image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
female 15644
54.3%
male 13162
45.7%

Most occurring characters

ValueCountFrequency (%)
E 44450
30.3%
M 28806
19.7%
A 28806
19.7%
L 28806
19.7%
F 15644
 
10.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 146512
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 44450
30.3%
M 28806
19.7%
A 28806
19.7%
L 28806
19.7%
F 15644
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 146512
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 44450
30.3%
M 28806
19.7%
A 28806
19.7%
L 28806
19.7%
F 15644
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 44450
30.3%
M 28806
19.7%
A 28806
19.7%
L 28806
19.7%
F 15644
 
10.7%
Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.8 KiB
JD
4808 
High School
4583 
MD
4161 
Masters
4141 
Associate
4125 
Other values (2)
7018 

Length

Max length11
Median length9
Mean length5.8733874
Min length2

Characters and Unicode

Total characters169365
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowJD
2nd rowJD
3rd rowJD
4th rowJD
5th rowHigh School

Common Values

ValueCountFrequency (%)
JD 4808
16.7%
High School 4583
15.9%
MD 4161
14.4%
Masters 4141
14.4%
Associate 4125
14.3%
PhD 3556
12.3%
College 3462
12.0%

Length

2023-03-16T06:17:34.925089image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:35.032658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
jd 4808
14.4%
high 4583
13.7%
school 4583
13.7%
md 4161
12.5%
masters 4141
12.4%
associate 4125
12.3%
phd 3556
10.6%
college 3462
10.4%

Most occurring characters

ValueCountFrequency (%)
o 16753
 
9.9%
s 16532
 
9.8%
e 15190
 
9.0%
h 12722
 
7.5%
D 12525
 
7.4%
l 11507
 
6.8%
i 8708
 
5.1%
c 8708
 
5.1%
M 8302
 
4.9%
a 8266
 
4.9%
Other values (10) 50152
29.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 118838
70.2%
Uppercase Letter 45944
 
27.1%
Space Separator 4583
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 16753
14.1%
s 16532
13.9%
e 15190
12.8%
h 12722
10.7%
l 11507
9.7%
i 8708
7.3%
c 8708
7.3%
a 8266
7.0%
t 8266
7.0%
g 8045
6.8%
Uppercase Letter
ValueCountFrequency (%)
D 12525
27.3%
M 8302
18.1%
J 4808
 
10.5%
S 4583
 
10.0%
H 4583
 
10.0%
A 4125
 
9.0%
P 3556
 
7.7%
C 3462
 
7.5%
Space Separator
ValueCountFrequency (%)
4583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 164782
97.3%
Common 4583
 
2.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 16753
 
10.2%
s 16532
 
10.0%
e 15190
 
9.2%
h 12722
 
7.7%
D 12525
 
7.6%
l 11507
 
7.0%
i 8708
 
5.3%
c 8708
 
5.3%
M 8302
 
5.0%
a 8266
 
5.0%
Other values (9) 45569
27.7%
Common
ValueCountFrequency (%)
4583
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 169365
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 16753
 
9.9%
s 16532
 
9.8%
e 15190
 
9.0%
h 12722
 
7.5%
D 12525
 
7.4%
l 11507
 
6.8%
i 8708
 
5.1%
c 8708
 
5.1%
M 8302
 
4.9%
a 8266
 
4.9%
Other values (10) 50152
29.6%
Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size254.1 KiB
machine-op-inspct
2798 
prof-specialty
2362 
tech-support
2268 
priv-house-serv
2176 
exec-managerial
2148 
Other values (9)
17084 

Length

Max length17
Median length16
Mean length13.541372
Min length5

Characters and Unicode

Total characters390479
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowarmed-forces
2nd rowtech-support
3rd rowarmed-forces
4th rowarmed-forces
5th rowexec-managerial

Common Values

ValueCountFrequency (%)
machine-op-inspct 2798
9.7%
prof-specialty 2362
 
8.2%
tech-support 2268
 
7.9%
priv-house-serv 2176
 
7.5%
exec-managerial 2148
 
7.4%
sales 2133
 
7.4%
craft-repair 2130
 
7.4%
transport-moving 2079
 
7.2%
armed-forces 2032
 
7.0%
other-service 1965
 
6.8%
Other values (4) 6745
23.4%

Length

2023-03-16T06:17:35.168216image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
machine-op-inspct 2798
9.7%
prof-specialty 2362
 
8.2%
tech-support 2268
 
7.9%
priv-house-serv 2176
 
7.5%
exec-managerial 2148
 
7.4%
sales 2133
 
7.4%
craft-repair 2130
 
7.4%
transport-moving 2079
 
7.2%
armed-forces 2032
 
7.0%
other-service 1965
 
6.8%
Other values (4) 6745
23.4%

Most occurring characters

ValueCountFrequency (%)
e 43937
11.3%
r 39606
 
10.1%
- 31677
 
8.1%
a 30536
 
7.8%
s 28417
 
7.3%
i 26991
 
6.9%
c 25538
 
6.5%
p 22956
 
5.9%
t 21379
 
5.5%
o 19474
 
5.0%
Other values (11) 99968
25.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 358802
91.9%
Dash Punctuation 31677
 
8.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 43937
12.2%
r 39606
11.0%
a 30536
 
8.5%
s 28417
 
7.9%
i 26991
 
7.5%
c 25538
 
7.1%
p 22956
 
6.4%
t 21379
 
6.0%
o 19474
 
5.4%
n 18114
 
5.0%
Other values (10) 81854
22.8%
Dash Punctuation
ValueCountFrequency (%)
- 31677
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 358802
91.9%
Common 31677
 
8.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 43937
12.2%
r 39606
11.0%
a 30536
 
8.5%
s 28417
 
7.9%
i 26991
 
7.5%
c 25538
 
7.1%
p 22956
 
6.4%
t 21379
 
6.0%
o 19474
 
5.4%
n 18114
 
5.0%
Other values (10) 81854
22.8%
Common
ValueCountFrequency (%)
- 31677
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 43937
11.3%
r 39606
 
10.1%
- 31677
 
8.1%
a 30536
 
7.8%
s 28417
 
7.3%
i 26991
 
6.9%
c 25538
 
6.5%
p 22956
 
5.9%
t 21379
 
5.5%
o 19474
 
5.0%
Other values (11) 99968
25.6%

InsuredHobbies
Categorical

Distinct20
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size254.1 KiB
bungie-jumping
 
1751
paintball
 
1688
camping
 
1681
kayaking
 
1611
exercise
 
1589
Other values (15)
20516 

Length

Max length14
Median length11
Mean length8.1953114
Min length4

Characters and Unicode

Total characters236320
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmovies
2nd rowcross-fit
3rd rowpolo
4th rowpolo
5th rowdancing

Common Values

ValueCountFrequency (%)
bungie-jumping 1751
 
6.1%
paintball 1688
 
5.9%
camping 1681
 
5.8%
kayaking 1611
 
5.6%
exercise 1589
 
5.5%
reading 1586
 
5.5%
movies 1529
 
5.3%
yachting 1486
 
5.2%
hiking 1483
 
5.1%
golf 1470
 
5.1%
Other values (10) 12962
45.0%

Length

2023-03-16T06:17:35.292574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bungie-jumping 1751
 
6.1%
paintball 1688
 
5.9%
camping 1681
 
5.8%
kayaking 1611
 
5.6%
exercise 1589
 
5.5%
reading 1586
 
5.5%
movies 1529
 
5.3%
yachting 1486
 
5.2%
hiking 1483
 
5.1%
base-jumping 1470
 
5.1%
Other values (10) 12962
45.0%

Most occurring characters

ValueCountFrequency (%)
i 27006
 
11.4%
g 20939
 
8.9%
a 20258
 
8.6%
e 19992
 
8.5%
n 19560
 
8.3%
s 15940
 
6.7%
o 9824
 
4.2%
l 9452
 
4.0%
m 9247
 
3.9%
p 9190
 
3.9%
Other values (14) 74912
31.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 229034
96.9%
Dash Punctuation 7286
 
3.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 27006
 
11.8%
g 20939
 
9.1%
a 20258
 
8.8%
e 19992
 
8.7%
n 19560
 
8.5%
s 15940
 
7.0%
o 9824
 
4.3%
l 9452
 
4.1%
m 9247
 
4.0%
p 9190
 
4.0%
Other values (13) 67626
29.5%
Dash Punctuation
ValueCountFrequency (%)
- 7286
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 229034
96.9%
Common 7286
 
3.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 27006
 
11.8%
g 20939
 
9.1%
a 20258
 
8.8%
e 19992
 
8.7%
n 19560
 
8.5%
s 15940
 
7.0%
o 9824
 
4.3%
l 9452
 
4.1%
m 9247
 
4.0%
p 9190
 
4.0%
Other values (13) 67626
29.5%
Common
ValueCountFrequency (%)
- 7286
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 236320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 27006
 
11.4%
g 20939
 
8.9%
a 20258
 
8.6%
e 19992
 
8.5%
n 19560
 
8.3%
s 15940
 
6.7%
o 9824
 
4.2%
l 9452
 
4.0%
m 9247
 
3.9%
p 9190
 
3.9%
Other values (14) 74912
31.7%

CapitalGains
Real number (ℝ)

Distinct338
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23066.57
Minimum0
Maximum100500
Zeros15819
Zeros (%)54.9%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:35.431975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q349000
95-th percentile70500
Maximum100500
Range100500
Interquartile range (IQR)49000

Descriptive statistics

Standard deviation27637.814
Coefficient of variation (CV)1.1981762
Kurtosis-1.1388804
Mean23066.57
Median Absolute Deviation (MAD)0
Skewness0.6207995
Sum6.651476 × 108
Variance7.6384875 × 108
MonotonicityNot monotonic
2023-03-16T06:17:35.587547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 15819
54.9%
44000 142
 
0.5%
51500 130
 
0.5%
55600 125
 
0.4%
75800 121
 
0.4%
63600 113
 
0.4%
46300 112
 
0.4%
49900 101
 
0.4%
51400 92
 
0.3%
68500 91
 
0.3%
Other values (328) 11990
41.6%
ValueCountFrequency (%)
0 15819
54.9%
800 33
 
0.1%
10000 29
 
0.1%
11000 22
 
0.1%
12100 48
 
0.2%
12800 26
 
0.1%
13100 56
 
0.2%
14100 27
 
0.1%
16100 35
 
0.1%
17300 44
 
0.2%
ValueCountFrequency (%)
100500 16
 
0.1%
98800 22
0.1%
94800 40
0.1%
91900 22
0.1%
90700 45
0.2%
88800 21
0.1%
88400 33
0.1%
87800 29
0.1%
84900 36
0.1%
83900 39
0.1%

CapitalLoss
Real number (ℝ)

Distinct354
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-24940.612
Minimum-111100
Maximum0
Zeros14759
Zeros (%)51.2%
Negative14077
Negative (%)48.8%
Memory size450.6 KiB
2023-03-16T06:17:35.738656image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-111100
5-th percentile-72000
Q1-50000
median0
Q30
95-th percentile0
Maximum0
Range111100
Interquartile range (IQR)50000

Descriptive statistics

Standard deviation27913.21
Coefficient of variation (CV)-1.119187
Kurtosis-1.2327114
Mean-24940.612
Median Absolute Deviation (MAD)0
Skewness-0.50366421
Sum-7.191875 × 108
Variance7.7914727 × 108
MonotonicityNot monotonic
2023-03-16T06:17:35.883260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 14759
51.2%
-53800 166
 
0.6%
-31700 148
 
0.5%
-61400 145
 
0.5%
-53700 138
 
0.5%
-32800 131
 
0.5%
-45100 128
 
0.4%
-45300 124
 
0.4%
-50300 120
 
0.4%
-45800 118
 
0.4%
Other values (344) 12859
44.6%
ValueCountFrequency (%)
-111100 22
0.1%
-93600 25
0.1%
-91400 15
 
0.1%
-91200 44
0.2%
-90600 53
0.2%
-90200 27
0.1%
-90100 30
0.1%
-89400 28
0.1%
-88300 23
0.1%
-87300 17
 
0.1%
ValueCountFrequency (%)
0 14759
51.2%
-5700 32
 
0.1%
-6300 36
 
0.1%
-8500 22
 
0.1%
-10600 25
 
0.1%
-12100 16
 
0.1%
-13200 29
 
0.1%
-13800 31
 
0.1%
-15600 17
 
0.1%
-15700 41
 
0.1%

CustomerLoyaltyPeriod
Real number (ℝ)

Distinct479
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.06787
Minimum1
Maximum479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:36.031920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile53
Q1126
median199
Q3267
95-th percentile395
Maximum479
Range478
Interquartile range (IQR)141

Descriptive statistics

Standard deviation99.932951
Coefficient of variation (CV)0.49211603
Kurtosis-0.30306084
Mean203.06787
Median Absolute Deviation (MAD)71
Skewness0.39452232
Sum5855665
Variance9986.5948
MonotonicityNot monotonic
2023-03-16T06:17:36.173466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 146
 
0.5%
254 145
 
0.5%
212 136
 
0.5%
154 134
 
0.5%
239 131
 
0.5%
124 127
 
0.4%
210 126
 
0.4%
258 125
 
0.4%
286 124
 
0.4%
257 124
 
0.4%
Other values (469) 27518
95.4%
ValueCountFrequency (%)
1 5
 
< 0.1%
2 6
 
< 0.1%
3 4
 
< 0.1%
4 5
 
< 0.1%
5 14
< 0.1%
6 14
< 0.1%
7 19
0.1%
8 34
0.1%
9 20
0.1%
10 17
0.1%
ValueCountFrequency (%)
479 2
 
< 0.1%
478 8
< 0.1%
477 4
 
< 0.1%
476 6
< 0.1%
475 14
< 0.1%
474 5
 
< 0.1%
473 6
< 0.1%
472 9
< 0.1%
471 8
< 0.1%
470 6
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
State3
10146 
State1
9716 
State2
8974 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters173016
Distinct characters7
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowState1
2nd rowState1
3rd rowState3
4th rowState2
5th rowState2

Common Values

ValueCountFrequency (%)
State3 10146
35.2%
State1 9716
33.7%
State2 8974
31.1%

Length

2023-03-16T06:17:36.253970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:36.356962image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
state3 10146
35.2%
state1 9716
33.7%
state2 8974
31.1%

Most occurring characters

ValueCountFrequency (%)
t 57672
33.3%
S 28836
16.7%
a 28836
16.7%
e 28836
16.7%
3 10146
 
5.9%
1 9716
 
5.6%
2 8974
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 115344
66.7%
Uppercase Letter 28836
 
16.7%
Decimal Number 28836
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 57672
50.0%
a 28836
25.0%
e 28836
25.0%
Decimal Number
ValueCountFrequency (%)
3 10146
35.2%
1 9716
33.7%
2 8974
31.1%
Uppercase Letter
ValueCountFrequency (%)
S 28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144180
83.3%
Common 28836
 
16.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 57672
40.0%
S 28836
20.0%
a 28836
20.0%
e 28836
20.0%
Common
ValueCountFrequency (%)
3 10146
35.2%
1 9716
33.7%
2 8974
31.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 57672
33.3%
S 28836
16.7%
a 28836
16.7%
e 28836
16.7%
3 10146
 
5.9%
1 9716
 
5.6%
2 8974
 
5.2%

Policy_Deductible
Real number (ℝ)

Distinct1496
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1114.2825
Minimum500
Maximum2000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:36.479389image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile500
Q1622
median1000
Q31627
95-th percentile2000
Maximum2000
Range1500
Interquartile range (IQR)1005

Descriptive statistics

Standard deviation546.63282
Coefficient of variation (CV)0.49056931
Kurtosis-1.1201301
Mean1114.2825
Median Absolute Deviation (MAD)441
Skewness0.56765381
Sum32131451
Variance298807.44
MonotonicityNot monotonic
2023-03-16T06:17:36.624194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 5292
18.4%
2000 5270
18.3%
1000 5064
 
17.6%
647 28
 
0.1%
722 27
 
0.1%
908 26
 
0.1%
950 26
 
0.1%
641 26
 
0.1%
964 25
 
0.1%
999 25
 
0.1%
Other values (1486) 13027
45.2%
ValueCountFrequency (%)
500 5292
18.4%
501 15
 
0.1%
502 18
 
0.1%
503 22
 
0.1%
504 16
 
0.1%
505 10
 
< 0.1%
506 15
 
0.1%
507 15
 
0.1%
508 18
 
0.1%
509 19
 
0.1%
ValueCountFrequency (%)
2000 5270
18.3%
1999 7
 
< 0.1%
1998 4
 
< 0.1%
1997 6
 
< 0.1%
1996 2
 
< 0.1%
1995 5
 
< 0.1%
1994 3
 
< 0.1%
1993 4
 
< 0.1%
1992 2
 
< 0.1%
1991 4
 
< 0.1%

PolicyAnnualPremium
Real number (ℝ)

Distinct23851
Distinct (%)83.1%
Missing141
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1261.7026
Minimum436.28
Maximum2047.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:36.788485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum436.28
5-th percentile922.613
Q11124.425
median1266.44
Q31397.2
95-th percentile1591.232
Maximum2047.59
Range1611.31
Interquartile range (IQR)272.775

Descriptive statistics

Standard deviation205.38516
Coefficient of variation (CV)0.16278412
Kurtosis0.12295146
Mean1261.7026
Median Absolute Deviation (MAD)136.45
Skewness0.012343957
Sum36204557
Variance42183.063
MonotonicityNot monotonic
2023-03-16T06:17:36.938887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1310.77 7
 
< 0.1%
1310.78 6
 
< 0.1%
1285.77 5
 
< 0.1%
1226.29 5
 
< 0.1%
1298.8 5
 
< 0.1%
1278.14 5
 
< 0.1%
1168.02 5
 
< 0.1%
1334.47 5
 
< 0.1%
1315.68 5
 
< 0.1%
1326.98 5
 
< 0.1%
Other values (23841) 28642
99.3%
(Missing) 141
 
0.5%
ValueCountFrequency (%)
436.28 1
< 0.1%
469.23 1
< 0.1%
484.67 1
< 0.1%
489.52 1
< 0.1%
491.56 1
< 0.1%
493.37 1
< 0.1%
493.58 1
< 0.1%
517.01 1
< 0.1%
532.04 1
< 0.1%
534.07 1
< 0.1%
ValueCountFrequency (%)
2047.59 1
< 0.1%
2043.56 1
< 0.1%
2041.82 1
< 0.1%
2014.2 1
< 0.1%
1999.97 1
< 0.1%
1998.54 1
< 0.1%
1986.6 1
< 0.1%
1977.79 1
< 0.1%
1969.63 1
< 0.1%
1965.03 1
< 0.1%

UmbrellaLimit
Real number (ℝ)

Distinct7089
Distinct (%)24.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean983668.03
Minimum-1000000
Maximum10000000
Zeros21073
Zeros (%)73.1%
Negative34
Negative (%)0.1%
Memory size450.6 KiB
2023-03-16T06:17:37.106183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-1000000
5-th percentile0
Q10
median0
Q3485961
95-th percentile5928631.5
Maximum10000000
Range11000000
Interquartile range (IQR)485961

Descriptive statistics

Standard deviation1969282
Coefficient of variation (CV)2.0019783
Kurtosis2.4918789
Mean983668.03
Median Absolute Deviation (MAD)0
Skewness1.9251235
Sum2.8365051 × 1010
Variance3.8780718 × 1012
MonotonicityNot monotonic
2023-03-16T06:17:37.257926image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 21073
73.1%
6000000 313
 
1.1%
5000000 139
 
0.5%
7000000 115
 
0.4%
4000000 71
 
0.2%
9000000 17
 
0.1%
3000000 9
 
< 0.1%
8000000 7
 
< 0.1%
2000000 3
 
< 0.1%
4234331 2
 
< 0.1%
Other values (7079) 7087
 
24.6%
ValueCountFrequency (%)
-1000000 1
< 0.1%
-994831 1
< 0.1%
-959396 1
< 0.1%
-934826 1
< 0.1%
-917296 1
< 0.1%
-902278 1
< 0.1%
-896857 1
< 0.1%
-872859 1
< 0.1%
-855720 1
< 0.1%
-843740 1
< 0.1%
ValueCountFrequency (%)
10000000 1
< 0.1%
9999481 1
< 0.1%
9957050 1
< 0.1%
9896244 1
< 0.1%
9805227 1
< 0.1%
9789696 1
< 0.1%
9675942 1
< 0.1%
9639501 1
< 0.1%
9634528 1
< 0.1%
9439351 1
< 0.1%
Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.7 KiB
own-child
5242 
not-in-family
5222 
other-relative
5153 
husband
5002 
wife
4224 

Length

Max length14
Median length13
Mean length9.5385282
Min length4

Characters and Unicode

Total characters275053
Distinct characters20
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownot-in-family
2nd rownot-in-family
3rd rowwife
4th rowown-child
5th rowunmarried

Common Values

ValueCountFrequency (%)
own-child 5242
18.2%
not-in-family 5222
18.1%
other-relative 5153
17.9%
husband 5002
17.3%
wife 4224
14.6%
unmarried 3993
13.8%

Length

2023-03-16T06:17:37.385711image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:37.525537image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
own-child 5242
18.2%
not-in-family 5222
18.1%
other-relative 5153
17.9%
husband 5002
17.3%
wife 4224
14.6%
unmarried 3993
13.8%

Most occurring characters

ValueCountFrequency (%)
i 29056
 
10.6%
n 24681
 
9.0%
e 23676
 
8.6%
- 20839
 
7.6%
a 19370
 
7.0%
r 18292
 
6.7%
o 15617
 
5.7%
l 15617
 
5.7%
t 15528
 
5.6%
h 15397
 
5.6%
Other values (10) 76980
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 254214
92.4%
Dash Punctuation 20839
 
7.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 29056
11.4%
n 24681
 
9.7%
e 23676
 
9.3%
a 19370
 
7.6%
r 18292
 
7.2%
o 15617
 
6.1%
l 15617
 
6.1%
t 15528
 
6.1%
h 15397
 
6.1%
d 14237
 
5.6%
Other values (9) 62743
24.7%
Dash Punctuation
ValueCountFrequency (%)
- 20839
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 254214
92.4%
Common 20839
 
7.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 29056
11.4%
n 24681
 
9.7%
e 23676
 
9.3%
a 19370
 
7.6%
r 18292
 
7.2%
o 15617
 
6.1%
l 15617
 
6.1%
t 15528
 
6.1%
h 15397
 
6.1%
d 14237
 
5.6%
Other values (9) 62743
24.7%
Common
ValueCountFrequency (%)
- 20839
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 275053
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 29056
 
10.6%
n 24681
 
9.0%
e 23676
 
8.6%
- 20839
 
7.6%
a 19370
 
7.0%
r 18292
 
6.7%
o 15617
 
5.7%
l 15617
 
5.7%
t 15528
 
5.6%
h 15397
 
5.6%
Other values (10) 76980
28.0%

TypeOfIncident
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
Multi-vehicle Collision
11966 
Single Vehicle Collision
11677 
Vehicle Theft
2685 
Parked Car
2508 

Length

Max length24
Median length23
Mean length21.343147
Min length10

Characters and Unicode

Total characters615451
Distinct characters25
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMulti-vehicle Collision
2nd rowMulti-vehicle Collision
3rd rowSingle Vehicle Collision
4th rowSingle Vehicle Collision
5th rowSingle Vehicle Collision

Common Values

ValueCountFrequency (%)
Multi-vehicle Collision 11966
41.5%
Single Vehicle Collision 11677
40.5%
Vehicle Theft 2685
 
9.3%
Parked Car 2508
 
8.7%

Length

2023-03-16T06:17:37.623549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:37.752800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
collision 23643
34.1%
vehicle 14362
20.7%
multi-vehicle 11966
17.3%
single 11677
16.8%
theft 2685
 
3.9%
parked 2508
 
3.6%
car 2508
 
3.6%

Most occurring characters

ValueCountFrequency (%)
l 97257
15.8%
i 97257
15.8%
e 69526
11.3%
o 47286
 
7.7%
40513
 
6.6%
n 35320
 
5.7%
h 29013
 
4.7%
c 26328
 
4.3%
C 26151
 
4.2%
s 23643
 
3.8%
Other values (15) 123157
20.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 493623
80.2%
Uppercase Letter 69349
 
11.3%
Space Separator 40513
 
6.6%
Dash Punctuation 11966
 
1.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 97257
19.7%
i 97257
19.7%
e 69526
14.1%
o 47286
9.6%
n 35320
 
7.2%
h 29013
 
5.9%
c 26328
 
5.3%
s 23643
 
4.8%
t 14651
 
3.0%
u 11966
 
2.4%
Other values (7) 41376
8.4%
Uppercase Letter
ValueCountFrequency (%)
C 26151
37.7%
V 14362
20.7%
M 11966
17.3%
S 11677
16.8%
T 2685
 
3.9%
P 2508
 
3.6%
Space Separator
ValueCountFrequency (%)
40513
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11966
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 562972
91.5%
Common 52479
 
8.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 97257
17.3%
i 97257
17.3%
e 69526
12.3%
o 47286
8.4%
n 35320
 
6.3%
h 29013
 
5.2%
c 26328
 
4.7%
C 26151
 
4.6%
s 23643
 
4.2%
t 14651
 
2.6%
Other values (13) 96540
17.1%
Common
ValueCountFrequency (%)
40513
77.2%
- 11966
 
22.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 615451
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 97257
15.8%
i 97257
15.8%
e 69526
11.3%
o 47286
 
7.7%
40513
 
6.6%
n 35320
 
5.7%
h 29013
 
4.7%
c 26328
 
4.3%
C 26151
 
4.2%
s 23643
 
3.8%
Other values (15) 123157
20.0%

TypeOfCollission
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing5162
Missing (%)17.9%
Memory size253.6 KiB
Rear Collision
8561 
Side Collision
7867 
Front Collision
7246 

Length

Max length15
Median length14
Mean length14.306074
Min length14

Characters and Unicode

Total characters338682
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSide Collision
2nd rowSide Collision
3rd rowSide Collision
4th rowSide Collision
5th rowRear Collision

Common Values

ValueCountFrequency (%)
Rear Collision 8561
29.7%
Side Collision 7867
27.3%
Front Collision 7246
25.1%
(Missing) 5162
17.9%

Length

2023-03-16T06:17:37.871043image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:37.988000image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
collision 23674
50.0%
rear 8561
 
18.1%
side 7867
 
16.6%
front 7246
 
15.3%

Most occurring characters

ValueCountFrequency (%)
i 55215
16.3%
o 54594
16.1%
l 47348
14.0%
n 30920
9.1%
23674
7.0%
C 23674
7.0%
s 23674
7.0%
e 16428
 
4.9%
r 15807
 
4.7%
R 8561
 
2.5%
Other values (5) 38787
11.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 267660
79.0%
Uppercase Letter 47348
 
14.0%
Space Separator 23674
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 55215
20.6%
o 54594
20.4%
l 47348
17.7%
n 30920
11.6%
s 23674
8.8%
e 16428
 
6.1%
r 15807
 
5.9%
a 8561
 
3.2%
d 7867
 
2.9%
t 7246
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
C 23674
50.0%
R 8561
 
18.1%
S 7867
 
16.6%
F 7246
 
15.3%
Space Separator
ValueCountFrequency (%)
23674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 315008
93.0%
Common 23674
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 55215
17.5%
o 54594
17.3%
l 47348
15.0%
n 30920
9.8%
C 23674
7.5%
s 23674
7.5%
e 16428
 
5.2%
r 15807
 
5.0%
R 8561
 
2.7%
a 8561
 
2.7%
Other values (4) 30226
9.6%
Common
ValueCountFrequency (%)
23674
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 338682
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 55215
16.3%
o 54594
16.1%
l 47348
14.0%
n 30920
9.1%
23674
7.0%
C 23674
7.0%
s 23674
7.0%
e 16428
 
4.9%
r 15807
 
4.7%
R 8561
 
2.5%
Other values (5) 38787
11.5%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
Minor Damage
10400 
Total Loss
8218 
Major Damage
7671 
Trivial Damage
2547 

Length

Max length14
Median length12
Mean length11.606672
Min length10

Characters and Unicode

Total characters334690
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTotal Loss
2nd rowTotal Loss
3rd rowMinor Damage
4th rowMinor Damage
5th rowMinor Damage

Common Values

ValueCountFrequency (%)
Minor Damage 10400
36.1%
Total Loss 8218
28.5%
Major Damage 7671
26.6%
Trivial Damage 2547
 
8.8%

Length

2023-03-16T06:17:38.104887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:38.244141image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
damage 20618
35.8%
minor 10400
18.0%
total 8218
 
14.2%
loss 8218
 
14.2%
major 7671
 
13.3%
trivial 2547
 
4.4%

Most occurring characters

ValueCountFrequency (%)
a 59672
17.8%
o 34507
10.3%
28836
 
8.6%
g 20618
 
6.2%
m 20618
 
6.2%
e 20618
 
6.2%
r 20618
 
6.2%
D 20618
 
6.2%
M 18071
 
5.4%
s 16436
 
4.9%
Other values (8) 74078
22.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 248182
74.2%
Uppercase Letter 57672
 
17.2%
Space Separator 28836
 
8.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 59672
24.0%
o 34507
13.9%
g 20618
 
8.3%
m 20618
 
8.3%
e 20618
 
8.3%
r 20618
 
8.3%
s 16436
 
6.6%
i 15494
 
6.2%
l 10765
 
4.3%
n 10400
 
4.2%
Other values (3) 18436
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
D 20618
35.8%
M 18071
31.3%
T 10765
18.7%
L 8218
 
14.2%
Space Separator
ValueCountFrequency (%)
28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 305854
91.4%
Common 28836
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 59672
19.5%
o 34507
11.3%
g 20618
 
6.7%
m 20618
 
6.7%
e 20618
 
6.7%
r 20618
 
6.7%
D 20618
 
6.7%
M 18071
 
5.9%
s 16436
 
5.4%
i 15494
 
5.1%
Other values (7) 58584
19.2%
Common
ValueCountFrequency (%)
28836
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 334690
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 59672
17.8%
o 34507
10.3%
28836
 
8.6%
g 20618
 
6.2%
m 20618
 
6.2%
e 20618
 
6.2%
r 20618
 
6.2%
D 20618
 
6.2%
M 18071
 
5.4%
s 16436
 
4.9%
Other values (8) 74078
22.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
Police
8324 
Fire
6518 
Ambulance
5732 
Other
5570 
None
2692 

Length

Max length9
Median length6
Mean length5.7643917
Min length4

Characters and Unicode

Total characters166222
Distinct characters18
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPolice
2nd rowPolice
3rd rowOther
4th rowOther
5th rowFire

Common Values

ValueCountFrequency (%)
Police 8324
28.9%
Fire 6518
22.6%
Ambulance 5732
19.9%
Other 5570
19.3%
None 2692
 
9.3%

Length

2023-03-16T06:17:38.362278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:38.494547image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
police 8324
28.9%
fire 6518
22.6%
ambulance 5732
19.9%
other 5570
19.3%
none 2692
 
9.3%

Most occurring characters

ValueCountFrequency (%)
e 28836
17.3%
i 14842
 
8.9%
l 14056
 
8.5%
c 14056
 
8.5%
r 12088
 
7.3%
o 11016
 
6.6%
n 8424
 
5.1%
P 8324
 
5.0%
F 6518
 
3.9%
u 5732
 
3.4%
Other values (8) 42330
25.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 137386
82.7%
Uppercase Letter 28836
 
17.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 28836
21.0%
i 14842
10.8%
l 14056
10.2%
c 14056
10.2%
r 12088
8.8%
o 11016
 
8.0%
n 8424
 
6.1%
u 5732
 
4.2%
a 5732
 
4.2%
m 5732
 
4.2%
Other values (3) 16872
12.3%
Uppercase Letter
ValueCountFrequency (%)
P 8324
28.9%
F 6518
22.6%
A 5732
19.9%
O 5570
19.3%
N 2692
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 166222
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 28836
17.3%
i 14842
 
8.9%
l 14056
 
8.5%
c 14056
 
8.5%
r 12088
 
7.3%
o 11016
 
6.6%
n 8424
 
5.1%
P 8324
 
5.0%
F 6518
 
3.9%
u 5732
 
3.4%
Other values (8) 42330
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 166222
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 28836
17.3%
i 14842
 
8.9%
l 14056
 
8.5%
c 14056
 
8.5%
r 12088
 
7.3%
o 11016
 
6.6%
n 8424
 
5.1%
P 8324
 
5.0%
F 6518
 
3.9%
u 5732
 
3.4%
Other values (8) 42330
25.5%

IncidentState
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.8 KiB
State5
7886 
State7
7168 
State9
6161 
State8
3181 
State4
3029 
Other values (2)
1411 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters173016
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowState7
2nd rowState7
3rd rowState8
4th rowState9
5th rowState8

Common Values

ValueCountFrequency (%)
State5 7886
27.3%
State7 7168
24.9%
State9 6161
21.4%
State8 3181
11.0%
State4 3029
 
10.5%
State6 810
 
2.8%
State3 601
 
2.1%

Length

2023-03-16T06:17:38.615353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:38.746229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
state5 7886
27.3%
state7 7168
24.9%
state9 6161
21.4%
state8 3181
11.0%
state4 3029
 
10.5%
state6 810
 
2.8%
state3 601
 
2.1%

Most occurring characters

ValueCountFrequency (%)
t 57672
33.3%
S 28836
16.7%
a 28836
16.7%
e 28836
16.7%
5 7886
 
4.6%
7 7168
 
4.1%
9 6161
 
3.6%
8 3181
 
1.8%
4 3029
 
1.8%
6 810
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 115344
66.7%
Uppercase Letter 28836
 
16.7%
Decimal Number 28836
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 7886
27.3%
7 7168
24.9%
9 6161
21.4%
8 3181
11.0%
4 3029
 
10.5%
6 810
 
2.8%
3 601
 
2.1%
Lowercase Letter
ValueCountFrequency (%)
t 57672
50.0%
a 28836
25.0%
e 28836
25.0%
Uppercase Letter
ValueCountFrequency (%)
S 28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144180
83.3%
Common 28836
 
16.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 7886
27.3%
7 7168
24.9%
9 6161
21.4%
8 3181
11.0%
4 3029
 
10.5%
6 810
 
2.8%
3 601
 
2.1%
Latin
ValueCountFrequency (%)
t 57672
40.0%
S 28836
20.0%
a 28836
20.0%
e 28836
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 173016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 57672
33.3%
S 28836
16.7%
a 28836
16.7%
e 28836
16.7%
5 7886
 
4.6%
7 7168
 
4.1%
9 6161
 
3.6%
8 3181
 
1.8%
4 3029
 
1.8%
6 810
 
0.5%

IncidentCity
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.8 KiB
City2
4486 
City1
4374 
City4
4311 
City7
4216 
City3
4073 
Other values (2)
7376 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters144180
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCity1
2nd rowCity5
3rd rowCity6
4th rowCity6
5th rowCity6

Common Values

ValueCountFrequency (%)
City2 4486
15.6%
City1 4374
15.2%
City4 4311
15.0%
City7 4216
14.6%
City3 4073
14.1%
City5 3698
12.8%
City6 3678
12.8%

Length

2023-03-16T06:17:38.871990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:38.986309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
city2 4486
15.6%
city1 4374
15.2%
city4 4311
15.0%
city7 4216
14.6%
city3 4073
14.1%
city5 3698
12.8%
city6 3678
12.8%

Most occurring characters

ValueCountFrequency (%)
C 28836
20.0%
i 28836
20.0%
t 28836
20.0%
y 28836
20.0%
2 4486
 
3.1%
1 4374
 
3.0%
4 4311
 
3.0%
7 4216
 
2.9%
3 4073
 
2.8%
5 3698
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 86508
60.0%
Uppercase Letter 28836
 
20.0%
Decimal Number 28836
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 4486
15.6%
1 4374
15.2%
4 4311
15.0%
7 4216
14.6%
3 4073
14.1%
5 3698
12.8%
6 3678
12.8%
Lowercase Letter
ValueCountFrequency (%)
i 28836
33.3%
t 28836
33.3%
y 28836
33.3%
Uppercase Letter
ValueCountFrequency (%)
C 28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 115344
80.0%
Common 28836
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 4486
15.6%
1 4374
15.2%
4 4311
15.0%
7 4216
14.6%
3 4073
14.1%
5 3698
12.8%
6 3678
12.8%
Latin
ValueCountFrequency (%)
C 28836
25.0%
i 28836
25.0%
t 28836
25.0%
y 28836
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 144180
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 28836
20.0%
i 28836
20.0%
t 28836
20.0%
y 28836
20.0%
2 4486
 
3.1%
1 4374
 
3.0%
4 4311
 
3.0%
7 4216
 
2.9%
3 4073
 
2.8%
5 3698
 
2.6%

IncidentAddress
Categorical

Distinct1000
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size321.7 KiB
Location 1341
 
73
Location 1254
 
72
Location 1227
 
71
Location 2006
 
70
Location 1136
 
68
Other values (995)
28482 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters374868
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLocation 1311
2nd rowLocation 1311
3rd rowLocation 2081
4th rowLocation 2081
5th rowLocation 1695

Common Values

ValueCountFrequency (%)
Location 1341 73
 
0.3%
Location 1254 72
 
0.2%
Location 1227 71
 
0.2%
Location 2006 70
 
0.2%
Location 1136 68
 
0.2%
Location 1192 67
 
0.2%
Location 1223 66
 
0.2%
Location 1321 66
 
0.2%
Location 1375 66
 
0.2%
Location 1469 65
 
0.2%
Other values (990) 28152
97.6%

Length

2023-03-16T06:17:39.049573image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
location 28836
50.0%
1341 73
 
0.1%
1254 72
 
0.1%
1227 71
 
0.1%
2006 70
 
0.1%
1136 68
 
0.1%
1192 67
 
0.1%
1223 66
 
0.1%
1321 66
 
0.1%
1375 66
 
0.1%
Other values (991) 28217
48.9%

Most occurring characters

ValueCountFrequency (%)
o 57672
15.4%
1 34985
9.3%
L 28836
7.7%
c 28836
7.7%
a 28836
7.7%
t 28836
7.7%
i 28836
7.7%
n 28836
7.7%
28836
7.7%
2 11064
 
3.0%
Other values (8) 69295
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 201852
53.8%
Decimal Number 115344
30.8%
Uppercase Letter 28836
 
7.7%
Space Separator 28836
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 34985
30.3%
2 11064
 
9.6%
0 10717
 
9.3%
4 8515
 
7.4%
7 8508
 
7.4%
6 8474
 
7.3%
9 8392
 
7.3%
5 8317
 
7.2%
8 8292
 
7.2%
3 8080
 
7.0%
Lowercase Letter
ValueCountFrequency (%)
o 57672
28.6%
c 28836
14.3%
a 28836
14.3%
t 28836
14.3%
i 28836
14.3%
n 28836
14.3%
Uppercase Letter
ValueCountFrequency (%)
L 28836
100.0%
Space Separator
ValueCountFrequency (%)
28836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 230688
61.5%
Common 144180
38.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 34985
24.3%
28836
20.0%
2 11064
 
7.7%
0 10717
 
7.4%
4 8515
 
5.9%
7 8508
 
5.9%
6 8474
 
5.9%
9 8392
 
5.8%
5 8317
 
5.8%
8 8292
 
5.8%
Latin
ValueCountFrequency (%)
o 57672
25.0%
L 28836
12.5%
c 28836
12.5%
a 28836
12.5%
t 28836
12.5%
i 28836
12.5%
n 28836
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 374868
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 57672
15.4%
1 34985
9.3%
L 28836
7.7%
c 28836
7.7%
a 28836
7.7%
t 28836
7.7%
i 28836
7.7%
n 28836
7.7%
28836
7.7%
2 11064
 
3.0%
Other values (8) 69295
18.5%

IncidentTime
Real number (ℝ)

Distinct24
Distinct (%)0.1%
Missing31
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean11.764069
Minimum0
Maximum23
Zeros391
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:39.151395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q16
median12
Q317
95-th percentile21
Maximum23
Range23
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.148869
Coefficient of variation (CV)0.52268217
Kurtosis-1.1049399
Mean11.764069
Median Absolute Deviation (MAD)5
Skewness-0.045473404
Sum338864
Variance37.808589
MonotonicityNot monotonic
2023-03-16T06:17:39.244185image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
16 1616
 
5.6%
17 1615
 
5.6%
6 1564
 
5.4%
15 1541
 
5.3%
14 1461
 
5.1%
5 1434
 
5.0%
13 1420
 
4.9%
18 1419
 
4.9%
4 1406
 
4.9%
12 1363
 
4.7%
Other values (14) 13966
48.4%
ValueCountFrequency (%)
0 391
 
1.4%
1 648
2.2%
2 713
2.5%
3 1232
4.3%
4 1406
4.9%
5 1434
5.0%
6 1564
5.4%
7 1331
4.6%
8 1251
4.3%
9 1210
4.2%
ValueCountFrequency (%)
23 526
 
1.8%
22 809
2.8%
21 971
3.4%
20 1153
4.0%
19 1280
4.4%
18 1419
4.9%
17 1615
5.6%
16 1616
5.6%
15 1541
5.3%
14 1461
5.1%

NumberOfVehicles
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.6 KiB
1
16169 
3
9711 
2
2276 
4
 
680

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28836
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

Length

2023-03-16T06:17:39.308995image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:39.418779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

Most occurring characters

ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28836
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
Common 28836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16169
56.1%
3 9711
33.7%
2 2276
 
7.9%
4 680
 
2.4%

PropertyDamage
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing10459
Missing (%)36.3%
Memory size253.6 KiB
NO
9687 
YES
8690 

Length

Max length3
Median length2
Mean length2.4728737
Min length2

Characters and Unicode

Total characters45444
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd rowYES
3rd rowYES
4th rowNO
5th rowNO

Common Values

ValueCountFrequency (%)
NO 9687
33.6%
YES 8690
30.1%
(Missing) 10459
36.3%

Length

2023-03-16T06:17:39.469841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:39.536494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 9687
52.7%
yes 8690
47.3%

Most occurring characters

ValueCountFrequency (%)
N 9687
21.3%
O 9687
21.3%
Y 8690
19.1%
E 8690
19.1%
S 8690
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 45444
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9687
21.3%
O 9687
21.3%
Y 8690
19.1%
E 8690
19.1%
S 8690
19.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 45444
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9687
21.3%
O 9687
21.3%
Y 8690
19.1%
E 8690
19.1%
S 8690
19.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45444
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9687
21.3%
O 9687
21.3%
Y 8690
19.1%
E 8690
19.1%
S 8690
19.1%

BodilyInjuries
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.6 KiB
1
11072 
0
9087 
2
8677 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28836
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Length

2023-03-16T06:17:39.634561image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:39.986116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Most occurring characters

ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28836
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common 28836
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11072
38.4%
0 9087
31.5%
2 8677
30.1%

Witnesses
Categorical

Distinct4
Distinct (%)< 0.1%
Missing46
Missing (%)0.2%
Memory size450.6 KiB
2.0
8449 
1.0
8345 
0.0
6128 
3.0
5868 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86370
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row3.0
4th row3.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0 8449
29.3%
1.0 8345
28.9%
0.0 6128
21.3%
3.0 5868
20.3%
(Missing) 46
 
0.2%

Length

2023-03-16T06:17:40.088329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:40.210260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 8449
29.3%
1.0 8345
29.0%
0.0 6128
21.3%
3.0 5868
20.4%

Most occurring characters

ValueCountFrequency (%)
0 34918
40.4%
. 28790
33.3%
2 8449
 
9.8%
1 8345
 
9.7%
3 5868
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57580
66.7%
Other Punctuation 28790
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34918
60.6%
2 8449
 
14.7%
1 8345
 
14.5%
3 5868
 
10.2%
Other Punctuation
ValueCountFrequency (%)
. 28790
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86370
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34918
40.4%
. 28790
33.3%
2 8449
 
9.8%
1 8345
 
9.7%
3 5868
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86370
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34918
40.4%
. 28790
33.3%
2 8449
 
9.8%
1 8345
 
9.7%
3 5868
 
6.8%

PoliceReport
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing9805
Missing (%)34.0%
Memory size253.6 KiB
NO
9898 
YES
9133 

Length

Max length3
Median length2
Mean length2.4799012
Min length2

Characters and Unicode

Total characters47195
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYES
2nd rowNO
3rd rowNO
4th rowYES
5th rowNO

Common Values

ValueCountFrequency (%)
NO 9898
34.3%
YES 9133
31.7%
(Missing) 9805
34.0%

Length

2023-03-16T06:17:40.323381image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:40.450196image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
no 9898
52.0%
yes 9133
48.0%

Most occurring characters

ValueCountFrequency (%)
N 9898
21.0%
O 9898
21.0%
Y 9133
19.4%
E 9133
19.4%
S 9133
19.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 47195
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 9898
21.0%
O 9898
21.0%
Y 9133
19.4%
E 9133
19.4%
S 9133
19.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 47195
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 9898
21.0%
O 9898
21.0%
Y 9133
19.4%
E 9133
19.4%
S 9133
19.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47195
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 9898
21.0%
O 9898
21.0%
Y 9133
19.4%
E 9133
19.4%
S 9133
19.4%

AmountOfTotalClaim
Real number (ℝ)

Distinct21975
Distinct (%)76.3%
Missing50
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean52308.545
Minimum150
Maximum114920
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:40.575298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile4615.25
Q144643.75
median58360
Q368982.75
95-th percentile84239.75
Maximum114920
Range114770
Interquartile range (IQR)24339

Descriptive statistics

Standard deviation25101.173
Coefficient of variation (CV)0.47986753
Kurtosis-0.29933491
Mean52308.545
Median Absolute Deviation (MAD)11737
Skewness-0.78261753
Sum1.5057538 × 109
Variance6.3006886 × 108
MonotonicityNot monotonic
2023-03-16T06:17:40.729317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6820 12
 
< 0.1%
5400 8
 
< 0.1%
60600 8
 
< 0.1%
55000 7
 
< 0.1%
5399 7
 
< 0.1%
63900 6
 
< 0.1%
5191 6
 
< 0.1%
5257 6
 
< 0.1%
4620 6
 
< 0.1%
5940 6
 
< 0.1%
Other values (21965) 28714
99.6%
(Missing) 50
 
0.2%
ValueCountFrequency (%)
150 1
< 0.1%
313 1
< 0.1%
334 1
< 0.1%
489 1
< 0.1%
547 1
< 0.1%
598 1
< 0.1%
681 1
< 0.1%
725 1
< 0.1%
812 1
< 0.1%
838 1
< 0.1%
ValueCountFrequency (%)
114920 1
< 0.1%
114141 1
< 0.1%
114113 1
< 0.1%
113997 1
< 0.1%
113771 1
< 0.1%
112817 1
< 0.1%
112560 1
< 0.1%
111870 1
< 0.1%
111771 1
< 0.1%
111708 1
< 0.1%

AmountOfInjuryClaim
Real number (ℝ)

Distinct11958
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7337.1184
Minimum0
Maximum21450
Zeros68
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:40.896072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile546
Q14743.75
median7147
Q310571.25
95-th percentile14720.75
Maximum21450
Range21450
Interquartile range (IQR)5827.5

Descriptive statistics

Standard deviation4427.6386
Coefficient of variation (CV)0.60345742
Kurtosis-0.70257905
Mean7337.1184
Median Absolute Deviation (MAD)3021
Skewness0.094468347
Sum2.1157315 × 108
Variance19603984
MonotonicityNot monotonic
2023-03-16T06:17:41.035325image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 68
 
0.2%
480 51
 
0.2%
640 34
 
0.1%
580 19
 
0.1%
536 17
 
0.1%
532 17
 
0.1%
740 17
 
0.1%
531 16
 
0.1%
600 16
 
0.1%
654 16
 
0.1%
Other values (11948) 28565
99.1%
ValueCountFrequency (%)
0 68
0.2%
3 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
16 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
21450 1
< 0.1%
21407 1
< 0.1%
21330 1
< 0.1%
21087 1
< 0.1%
21041 1
< 0.1%
20977 1
< 0.1%
20960 1
< 0.1%
20860 1
< 0.1%
20846 1
< 0.1%
20749 1
< 0.1%

AmountOfPropertyClaim
Real number (ℝ)

Distinct11785
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7283.8702
Minimum0
Maximum23670
Zeros35
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:41.224198image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile548
Q14862
median7051
Q310327
95-th percentile14672
Maximum23670
Range23670
Interquartile range (IQR)5465

Descriptive statistics

Standard deviation4375.8427
Coefficient of variation (CV)0.60075792
Kurtosis-0.44838688
Mean7283.8702
Median Absolute Deviation (MAD)2781
Skewness0.17557018
Sum2.1003768 × 108
Variance19148000
MonotonicityNot monotonic
2023-03-16T06:17:41.400941image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 35
 
0.1%
580 17
 
0.1%
578 16
 
0.1%
606 16
 
0.1%
506 15
 
0.1%
1240 15
 
0.1%
490 14
 
< 0.1%
717 14
 
< 0.1%
650 13
 
< 0.1%
461 13
 
< 0.1%
Other values (11775) 28668
99.4%
ValueCountFrequency (%)
0 35
0.1%
2 1
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
21 1
 
< 0.1%
22 2
 
< 0.1%
23 2
 
< 0.1%
25 1
 
< 0.1%
26 3
 
< 0.1%
27 1
 
< 0.1%
ValueCountFrequency (%)
23670 1
< 0.1%
23136 1
< 0.1%
22806 1
< 0.1%
22393 1
< 0.1%
21917 1
< 0.1%
21810 1
< 0.1%
21799 1
< 0.1%
21796 1
< 0.1%
21777 1
< 0.1%
21743 1
< 0.1%

AmountOfVehicleDamage
Real number (ℝ)

Distinct20041
Distinct (%)69.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37687.129
Minimum109
Maximum79560
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:41.616217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum109
5-th percentile3292
Q132193.25
median42457.5
Q349535.75
95-th percentile59936.5
Maximum79560
Range79451
Interquartile range (IQR)17342.5

Descriptive statistics

Standard deviation17977.048
Coefficient of variation (CV)0.47700763
Kurtosis-0.28611945
Mean37687.129
Median Absolute Deviation (MAD)8276.5
Skewness-0.81990803
Sum1.0867461 × 109
Variance3.2317426 × 108
MonotonicityNot monotonic
2023-03-16T06:17:41.831485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2560 19
 
0.1%
5040 12
 
< 0.1%
28000 11
 
< 0.1%
3600 8
 
< 0.1%
3290 8
 
< 0.1%
3933 8
 
< 0.1%
4317 7
 
< 0.1%
3680 7
 
< 0.1%
41999 7
 
< 0.1%
48160 7
 
< 0.1%
Other values (20031) 28742
99.7%
ValueCountFrequency (%)
109 1
< 0.1%
226 1
< 0.1%
236 1
< 0.1%
374 1
< 0.1%
418 1
< 0.1%
458 1
< 0.1%
524 1
< 0.1%
539 1
< 0.1%
544 1
< 0.1%
562 1
< 0.1%
ValueCountFrequency (%)
79560 1
< 0.1%
79058 1
< 0.1%
79022 1
< 0.1%
78820 1
< 0.1%
78793 1
< 0.1%
78158 1
< 0.1%
77987 1
< 0.1%
77691 1
< 0.1%
77670 2
< 0.1%
77571 1
< 0.1%

VehicleMake
Categorical

Distinct14
Distinct (%)< 0.1%
Missing50
Missing (%)0.2%
Memory size254.1 KiB
Saab
2415 
Suburu
2313 
Nissan
2300 
Dodge
2263 
Chevrolet
2174 
Other values (9)
17321 

Length

Max length10
Median length9
Mean length5.6791148
Min length3

Characters and Unicode

Total characters163479
Distinct characters33
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAudi
2nd rowAudi
3rd rowVolkswagen
4th rowVolkswagen
5th rowToyota

Common Values

ValueCountFrequency (%)
Saab 2415
 
8.4%
Suburu 2313
 
8.0%
Nissan 2300
 
8.0%
Dodge 2263
 
7.8%
Chevrolet 2174
 
7.5%
Ford 2158
 
7.5%
Accura 2099
 
7.3%
BMW 2073
 
7.2%
Toyota 1981
 
6.9%
Volkswagen 1960
 
6.8%
Other values (4) 7050
24.4%

Length

2023-03-16T06:17:42.008746image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
saab 2415
 
8.4%
suburu 2313
 
8.0%
nissan 2300
 
8.0%
dodge 2263
 
7.9%
chevrolet 2174
 
7.6%
ford 2158
 
7.5%
accura 2099
 
7.3%
bmw 2073
 
7.2%
toyota 1981
 
6.9%
volkswagen 1960
 
6.8%
Other values (4) 7050
24.5%

Most occurring characters

ValueCountFrequency (%)
e 17440
 
10.7%
a 14663
 
9.0%
o 14010
 
8.6%
u 10990
 
6.7%
r 10403
 
6.4%
d 9525
 
5.8%
s 8219
 
5.0%
c 5857
 
3.6%
n 5753
 
3.5%
S 4728
 
2.9%
Other values (23) 61891
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130547
79.9%
Uppercase Letter 32932
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 17440
13.4%
a 14663
11.2%
o 14010
10.7%
u 10990
 
8.4%
r 10403
 
8.0%
d 9525
 
7.3%
s 8219
 
6.3%
c 5857
 
4.5%
n 5753
 
4.4%
b 4728
 
3.6%
Other values (10) 28959
22.2%
Uppercase Letter
ValueCountFrequency (%)
S 4728
14.4%
A 4051
12.3%
M 3732
11.3%
N 2300
7.0%
D 2263
6.9%
C 2174
6.6%
F 2158
 
6.6%
B 2073
 
6.3%
W 2073
 
6.3%
T 1981
 
6.0%
Other values (3) 5399
16.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 163479
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 17440
 
10.7%
a 14663
 
9.0%
o 14010
 
8.6%
u 10990
 
6.7%
r 10403
 
6.4%
d 9525
 
5.8%
s 8219
 
5.0%
c 5857
 
3.6%
n 5753
 
3.5%
S 4728
 
2.9%
Other values (23) 61891
37.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 163479
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 17440
 
10.7%
a 14663
 
9.0%
o 14010
 
8.6%
u 10990
 
6.7%
r 10403
 
6.4%
d 9525
 
5.8%
s 8219
 
5.0%
c 5857
 
3.6%
n 5753
 
3.5%
S 4728
 
2.9%
Other values (23) 61891
37.9%

VehicleModel
Categorical

Distinct39
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size254.8 KiB
RAM
 
1344
Wrangler
 
1261
A3
 
1102
MDX
 
1054
Jetta
 
1037
Other values (34)
23038 

Length

Max length14
Median length9
Mean length5.1497087
Min length2

Characters and Unicode

Total characters148497
Distinct characters52
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA5
2nd rowA5
3rd rowJetta
4th rowJetta
5th rowCRV

Common Values

ValueCountFrequency (%)
RAM 1344
 
4.7%
Wrangler 1261
 
4.4%
A3 1102
 
3.8%
MDX 1054
 
3.7%
Jetta 1037
 
3.6%
Neon 928
 
3.2%
Pathfinder 919
 
3.2%
Passat 888
 
3.1%
Legacy 887
 
3.1%
92x 859
 
3.0%
Other values (29) 18557
64.4%

Length

2023-03-16T06:17:42.171864image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ram 1344
 
4.5%
wrangler 1261
 
4.2%
a3 1102
 
3.7%
mdx 1054
 
3.5%
jetta 1037
 
3.5%
neon 928
 
3.1%
pathfinder 919
 
3.1%
passat 888
 
3.0%
legacy 887
 
3.0%
92x 859
 
2.9%
Other values (31) 19711
65.7%

Most occurring characters

ValueCountFrequency (%)
a 14174
 
9.5%
e 12147
 
8.2%
r 11139
 
7.5%
i 6762
 
4.6%
o 6638
 
4.5%
t 5363
 
3.6%
l 5148
 
3.5%
n 5109
 
3.4%
M 5056
 
3.4%
s 4352
 
2.9%
Other values (42) 72609
48.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95721
64.5%
Uppercase Letter 35108
 
23.6%
Decimal Number 16514
 
11.1%
Space Separator 1154
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14174
14.8%
e 12147
12.7%
r 11139
11.6%
i 6762
 
7.1%
o 6638
 
6.9%
t 5363
 
5.6%
l 5148
 
5.4%
n 5109
 
5.3%
s 4352
 
4.5%
d 3248
 
3.4%
Other values (13) 21641
22.6%
Uppercase Letter
ValueCountFrequency (%)
M 5056
14.4%
C 3642
10.4%
A 3568
10.2%
X 2596
 
7.4%
R 2283
 
6.5%
F 2231
 
6.4%
L 2170
 
6.2%
P 1807
 
5.1%
S 1501
 
4.3%
T 1420
 
4.0%
Other values (10) 8834
25.2%
Decimal Number
ValueCountFrequency (%)
5 4228
25.6%
0 3740
22.6%
3 3338
20.2%
9 2403
14.6%
2 859
 
5.2%
1 797
 
4.8%
4 695
 
4.2%
6 454
 
2.7%
Space Separator
ValueCountFrequency (%)
1154
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 130829
88.1%
Common 17668
 
11.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14174
 
10.8%
e 12147
 
9.3%
r 11139
 
8.5%
i 6762
 
5.2%
o 6638
 
5.1%
t 5363
 
4.1%
l 5148
 
3.9%
n 5109
 
3.9%
M 5056
 
3.9%
s 4352
 
3.3%
Other values (33) 54941
42.0%
Common
ValueCountFrequency (%)
5 4228
23.9%
0 3740
21.2%
3 3338
18.9%
9 2403
13.6%
1154
 
6.5%
2 859
 
4.9%
1 797
 
4.5%
4 695
 
3.9%
6 454
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 148497
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14174
 
9.5%
e 12147
 
8.2%
r 11139
 
7.5%
i 6762
 
4.6%
o 6638
 
4.5%
t 5363
 
3.6%
l 5148
 
3.5%
n 5109
 
3.4%
M 5056
 
3.4%
s 4352
 
2.9%
Other values (42) 72609
48.9%

ReportedFraud
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
N
21051 
Y
7785 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters28836
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowN
2nd rowN
3rd rowN
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 21051
73.0%
Y 7785
 
27.0%

Length

2023-03-16T06:17:42.316207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:42.454453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
n 21051
73.0%
y 7785
 
27.0%

Most occurring characters

ValueCountFrequency (%)
N 21051
73.0%
Y 7785
 
27.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 28836
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 21051
73.0%
Y 7785
 
27.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 28836
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 21051
73.0%
Y 7785
 
27.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28836
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 21051
73.0%
Y 7785
 
27.0%

SplitLimit
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.6 KiB
250
10237 
100
9859 
500
8740 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters86508
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row500
4th row500
5th row100

Common Values

ValueCountFrequency (%)
250 10237
35.5%
100 9859
34.2%
500 8740
30.3%

Length

2023-03-16T06:17:42.581279image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:42.755611image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
250 10237
35.5%
100 9859
34.2%
500 8740
30.3%

Most occurring characters

ValueCountFrequency (%)
0 47435
54.8%
5 18977
21.9%
2 10237
 
11.8%
1 9859
 
11.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86508
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 47435
54.8%
5 18977
21.9%
2 10237
 
11.8%
1 9859
 
11.4%

Most occurring scripts

ValueCountFrequency (%)
Common 86508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 47435
54.8%
5 18977
21.9%
2 10237
 
11.8%
1 9859
 
11.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 47435
54.8%
5 18977
21.9%
2 10237
 
11.8%
1 9859
 
11.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size450.6 KiB
500
10094 
300
9942 
1000
8800 

Length

Max length4
Median length3
Mean length3.3051741
Min length3

Characters and Unicode

Total characters95308
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row300
2nd row300
3rd row1000
4th row1000
5th row300

Common Values

ValueCountFrequency (%)
500 10094
35.0%
300 9942
34.5%
1000 8800
30.5%

Length

2023-03-16T06:17:42.870107image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:43.021181image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
500 10094
35.0%
300 9942
34.5%
1000 8800
30.5%

Most occurring characters

ValueCountFrequency (%)
0 66472
69.7%
5 10094
 
10.6%
3 9942
 
10.4%
1 8800
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95308
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66472
69.7%
5 10094
 
10.6%
3 9942
 
10.4%
1 8800
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 95308
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66472
69.7%
5 10094
 
10.6%
3 9942
 
10.4%
1 8800
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95308
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66472
69.7%
5 10094
 
10.6%
3 9942
 
10.4%
1 8800
 
9.2%

VehicleAge
Real number (ℝ)

Distinct1295
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3647.6998
Minimum0
Maximum7363
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size450.6 KiB
2023-03-16T06:17:43.175600image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile736
Q11884.75
median3671
Q35163
95-th percentile6632
Maximum7363
Range7363
Interquartile range (IQR)3278.25

Descriptive statistics

Standard deviation1939.6937
Coefficient of variation (CV)0.53175804
Kurtosis-1.0480455
Mean3647.6998
Median Absolute Deviation (MAD)1492
Skewness0.046000191
Sum1.0518507 × 108
Variance3762411.6
MonotonicityNot monotonic
2023-03-16T06:17:43.336161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1494 76
 
0.3%
4028 74
 
0.3%
2938 70
 
0.2%
1843 69
 
0.2%
4799 68
 
0.2%
3698 64
 
0.2%
4027 62
 
0.2%
2573 62
 
0.2%
4433 62
 
0.2%
2970 62
 
0.2%
Other values (1285) 28167
97.7%
ValueCountFrequency (%)
0 12
< 0.1%
1 11
< 0.1%
3 3
 
< 0.1%
4 1
 
< 0.1%
5 6
< 0.1%
6 4
 
< 0.1%
7 14
< 0.1%
8 10
< 0.1%
9 4
 
< 0.1%
14 5
 
< 0.1%
ValueCountFrequency (%)
7363 6
 
< 0.1%
7362 2
 
< 0.1%
7361 5
 
< 0.1%
7359 9
 
< 0.1%
7358 11
 
< 0.1%
7357 19
0.1%
7356 28
0.1%
7355 5
 
< 0.1%
7354 5
 
< 0.1%
7353 6
 
< 0.1%

DayOfWeek
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.8 KiB
Friday
4376 
Tuesday
4230 
Thursday
4156 
Saturday
4131 
Wednesday
4014 
Other values (2)
7929 

Length

Max length9
Median length8
Mean length7.1390623
Min length6

Characters and Unicode

Total characters205862
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTuesday
2nd rowMonday
3rd rowThursday
4th rowMonday
5th rowFriday

Common Values

ValueCountFrequency (%)
Friday 4376
15.2%
Tuesday 4230
14.7%
Thursday 4156
14.4%
Saturday 4131
14.3%
Wednesday 4014
13.9%
Monday 3969
13.8%
Sunday 3960
13.7%

Length

2023-03-16T06:17:43.517096image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:43.689422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
friday 4376
15.2%
tuesday 4230
14.7%
thursday 4156
14.4%
saturday 4131
14.3%
wednesday 4014
13.9%
monday 3969
13.8%
sunday 3960
13.7%

Most occurring characters

ValueCountFrequency (%)
a 32967
16.0%
d 32850
16.0%
y 28836
14.0%
u 16477
8.0%
r 12663
 
6.2%
s 12400
 
6.0%
e 12258
 
6.0%
n 11943
 
5.8%
T 8386
 
4.1%
S 8091
 
3.9%
Other values (7) 28991
14.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 177026
86.0%
Uppercase Letter 28836
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 32967
18.6%
d 32850
18.6%
y 28836
16.3%
u 16477
9.3%
r 12663
 
7.2%
s 12400
 
7.0%
e 12258
 
6.9%
n 11943
 
6.7%
i 4376
 
2.5%
h 4156
 
2.3%
Other values (2) 8100
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
T 8386
29.1%
S 8091
28.1%
F 4376
15.2%
W 4014
13.9%
M 3969
13.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 205862
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 32967
16.0%
d 32850
16.0%
y 28836
14.0%
u 16477
8.0%
r 12663
 
6.2%
s 12400
 
6.0%
e 12258
 
6.0%
n 11943
 
5.8%
T 8386
 
4.1%
S 8091
 
3.9%
Other values (7) 28991
14.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 205862
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 32967
16.0%
d 32850
16.0%
y 28836
14.0%
u 16477
8.0%
r 12663
 
6.2%
s 12400
 
6.0%
e 12258
 
6.0%
n 11943
 
5.8%
T 8386
 
4.1%
S 8091
 
3.9%
Other values (7) 28991
14.1%

MonthOfIncident
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size253.6 KiB
January
14796 
February
13803 
March
 
237

Length

Max length8
Median length7
Mean length7.4622347
Min length5

Characters and Unicode

Total characters215181
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFebruary
2nd rowFebruary
3rd rowJanuary
4th rowJanuary
5th rowJanuary

Common Values

ValueCountFrequency (%)
January 14796
51.3%
February 13803
47.9%
March 237
 
0.8%

Length

2023-03-16T06:17:43.861037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-16T06:17:44.017518image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
january 14796
51.3%
february 13803
47.9%
march 237
 
0.8%

Most occurring characters

ValueCountFrequency (%)
a 43632
20.3%
r 42639
19.8%
u 28599
13.3%
y 28599
13.3%
J 14796
 
6.9%
n 14796
 
6.9%
F 13803
 
6.4%
e 13803
 
6.4%
b 13803
 
6.4%
M 237
 
0.1%
Other values (2) 474
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 186345
86.6%
Uppercase Letter 28836
 
13.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 43632
23.4%
r 42639
22.9%
u 28599
15.3%
y 28599
15.3%
n 14796
 
7.9%
e 13803
 
7.4%
b 13803
 
7.4%
c 237
 
0.1%
h 237
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
J 14796
51.3%
F 13803
47.9%
M 237
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 215181
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 43632
20.3%
r 42639
19.8%
u 28599
13.3%
y 28599
13.3%
J 14796
 
6.9%
n 14796
 
6.9%
F 13803
 
6.4%
e 13803
 
6.4%
b 13803
 
6.4%
M 237
 
0.1%
Other values (2) 474
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 215181
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 43632
20.3%
r 42639
19.8%
u 28599
13.3%
y 28599
13.3%
J 14796
 
6.9%
n 14796
 
6.9%
F 13803
 
6.4%
e 13803
 
6.4%
b 13803
 
6.4%
M 237
 
0.1%
Other values (2) 474
 
0.2%
Distinct7358
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4775.393
Minimum-20
Maximum9176
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)< 0.1%
Memory size450.6 KiB
2023-03-16T06:17:44.159970image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-20
5-th percentile925
Q12792.75
median4811
Q36800
95-th percentile8500
Maximum9176
Range9196
Interquartile range (IQR)4007.25

Descriptive statistics

Standard deviation2396.1441
Coefficient of variation (CV)0.50176898
Kurtosis-1.0972806
Mean4775.393
Median Absolute Deviation (MAD)2004
Skewness-0.047200298
Sum1.3770323 × 108
Variance5741506.3
MonotonicityNot monotonic
2023-03-16T06:17:44.331207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3495 27
 
0.1%
3866 25
 
0.1%
7828 24
 
0.1%
5321 24
 
0.1%
4203 24
 
0.1%
2161 22
 
0.1%
9064 22
 
0.1%
2441 22
 
0.1%
5972 21
 
0.1%
7942 20
 
0.1%
Other values (7348) 28605
99.2%
ValueCountFrequency (%)
-20 1
 
< 0.1%
-19 1
 
< 0.1%
5 1
 
< 0.1%
6 3
< 0.1%
7 2
< 0.1%
11 1
 
< 0.1%
12 3
< 0.1%
13 1
 
< 0.1%
16 1
 
< 0.1%
22 1
 
< 0.1%
ValueCountFrequency (%)
9176 1
 
< 0.1%
9175 2
< 0.1%
9172 3
< 0.1%
9171 2
< 0.1%
9155 1
 
< 0.1%
9154 1
 
< 0.1%
9152 3
< 0.1%
9140 4
< 0.1%
9139 1
 
< 0.1%
9133 3
< 0.1%

Interactions

2023-03-16T06:17:29.940967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:58.941505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.826843image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.930579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.778232image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.809344image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.646208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.975956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.677882image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.826925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.725502image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.858356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.077621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.129305image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.472952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:30.070466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.066804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.962642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.058137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.904371image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.923506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.822036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:12.158040image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.801931image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.949051image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.843270image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.999911image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.216560image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.269545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.631073image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:30.238015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.217167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.106707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.205294image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.008217image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.999081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.004713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:12.334682image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.950022image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.086362image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.991116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.164447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.386878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.463576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.792744image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:30.636026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.342867image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.246132image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.296287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.135760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.119057image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.137839image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:12.499429image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.096038image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.206841image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.075987image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.305551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.532749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.650934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.942747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:30.768351image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.468224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.385760image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.414136image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.235169image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.189199image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.272094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:12.662635image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.286123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.343226image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.205834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.436986image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.663379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.800942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.106980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:30.908622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.557207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.525086image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.545932image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.359436image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.307301image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.417447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:12.852100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.413522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.474018image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.322973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.607907image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.783920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:25.973498image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.257850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.060749image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.674017image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.677616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.677984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.499468image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.437690image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.574450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:13.059397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.560738image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.612072image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.459223image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.756342image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:23.945887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.129482image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.442094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.237827image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.812764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.830890image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.794806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.627557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.544638image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.719252image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:13.434385image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.718630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.768975image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.597645image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:21.908618image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.106636image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.290919image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.618378image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.392056image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:16:59.940069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:01.971507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:03.927269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.727758image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.665727image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:10.863535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:13.575037image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.857367image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:17.902510image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.683713image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.056475image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.242624image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.432513image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.776657image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.517663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.063968image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.104584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.060059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:05.854452image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.780866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.005942image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:13.727722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:15.987979image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.040838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.800863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.192496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.356741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.541935image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:28.971887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.659311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.187357image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.247189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.192094image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.208191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:08.875231image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.183069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:13.873741image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.134769image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.150580image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:19.925967image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.344075image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.485117image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.681904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:29.152191image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.796704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.310679image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.384870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.317200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.311380image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.031661image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.311063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.018329image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.261082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.261799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.054866image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.485971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.598121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.813470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:29.284509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:31.918750image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.437936image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.530772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.422971image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.428379image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.159951image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.447958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.196835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.389161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.374863image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.175680image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.629871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.715576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:26.981112image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:29.441052image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:32.058102image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.565015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.649139image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.567616image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.563229image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.318943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.635920image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.350706image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.541948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.506952image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.351101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.799538image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.857138image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.149496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:29.625244image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:32.201629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:00.701041image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:02.795382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:04.653165image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:07.692177image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:09.502174image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:11.828763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:14.521200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:16.689855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:18.605562image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:20.709640image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:22.951766image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:24.995576image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:27.318179image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-03-16T06:17:29.770853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-03-16T06:17:44.526799image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
InsuredAgeInsuredZipCodeCapitalGainsCapitalLossCustomerLoyaltyPeriodPolicy_DeductiblePolicyAnnualPremiumUmbrellaLimitIncidentTimeAmountOfTotalClaimAmountOfInjuryClaimAmountOfPropertyClaimAmountOfVehicleDamageVehicleAgeTimeBetweenCoverageAndIncidentInsuredGenderInsuredEducationLevelInsuredOccupationInsuredHobbiesInsurancePolicyStateInsuredRelationshipTypeOfIncidentTypeOfCollissionSeverityOfIncidentAuthoritiesContactedIncidentStateIncidentCityNumberOfVehiclesPropertyDamageBodilyInjuriesWitnessesPoliceReportVehicleMakeVehicleModelReportedFraudSplitLimitCombinedSingleLimitDayOfWeekMonthOfIncident
InsuredAge1.0000.034-0.0230.0110.9260.0390.063-0.0100.1200.0730.0890.0750.0510.0010.0460.0880.0530.0550.0760.0450.0500.0590.0710.0650.0580.0400.0490.0620.0650.0580.0710.0430.0590.0960.0280.0390.0390.0300.064
InsuredZipCode0.0341.000-0.0050.0270.0400.0040.0330.0160.0040.0150.0050.0090.0040.0110.0050.6500.6040.5750.5720.6380.5940.8320.6580.7270.6930.5950.5960.6460.7000.6750.6040.7060.5970.5800.8360.7210.7190.2050.677
CapitalGains-0.023-0.0051.000-0.048-0.0080.020-0.007-0.046-0.0130.0030.0050.0030.004-0.025-0.0420.0320.0650.0660.0880.0600.0570.0770.0800.0640.0610.0580.0480.0690.0680.0860.0630.0550.0710.1210.0770.0560.0630.0170.067
CapitalLoss0.0110.027-0.0481.0000.016-0.0410.013-0.019-0.022-0.020-0.025-0.018-0.0130.0640.0480.0730.0620.0730.0810.0590.0620.0800.0850.0720.0730.0610.0600.0590.1200.0670.0570.0560.0700.1170.0310.0600.0570.0170.070
CustomerLoyaltyPeriod0.9260.040-0.0080.0161.0000.0400.050-0.0030.0980.0620.0770.0500.0480.0020.0460.0820.0520.0590.0740.0390.0490.0570.0640.0620.0580.0380.0480.0550.0700.0650.0660.0700.0590.0950.0420.0450.0490.0350.067
Policy_Deductible0.0390.0040.020-0.0410.0401.000-0.015-0.0120.0890.0220.0270.0640.003-0.0270.0590.0360.0300.0520.0460.0220.0270.0480.0420.0300.0310.0390.0360.0620.0470.0470.0590.0460.0410.0780.0470.0380.0430.0200.040
PolicyAnnualPremium0.0630.033-0.0070.0130.050-0.0151.000-0.002-0.009-0.005-0.022-0.0100.0010.0160.0010.0910.0520.0550.0700.0490.0370.0530.0400.0560.0500.0480.0590.0490.0780.0490.0560.0560.0560.0940.0580.0680.0630.0340.057
UmbrellaLimit-0.0100.016-0.046-0.019-0.003-0.012-0.0021.000-0.022-0.032-0.039-0.000-0.030-0.006-0.0520.0270.0320.0430.0600.0420.0610.0490.0390.0420.0350.0470.0510.0530.0820.0870.0290.0630.0580.0840.0810.0390.0290.0240.042
IncidentTime0.1200.004-0.013-0.0220.0980.089-0.009-0.0221.0000.2140.2230.2010.214-0.043-0.0490.0250.0460.0550.0660.0490.0430.2470.0640.1830.1600.0620.0500.1360.0720.0660.0630.0820.0550.0860.0490.0410.0390.0240.068
AmountOfTotalClaim0.0730.0150.003-0.0200.0620.022-0.005-0.0320.2141.0000.7870.8010.9650.0280.0150.0630.0730.0690.0800.0500.0470.5690.0680.4200.3830.0680.0530.2480.0740.0710.0580.0690.0680.1200.1650.0670.0650.0320.059
AmountOfInjuryClaim0.0890.0050.005-0.0250.0770.027-0.022-0.0390.2230.7871.0000.5750.6800.0250.0050.0430.0600.0630.0830.0580.0600.5360.0430.3940.3660.0610.0500.2250.0780.0680.0640.0810.0650.1090.1370.0690.0660.0330.097
AmountOfPropertyClaim0.0750.0090.003-0.0180.0500.064-0.010-0.0000.2010.8010.5751.0000.700-0.0180.0130.0520.0650.0650.0840.0510.0420.5450.0560.4010.3660.0660.0570.2330.0660.0720.0700.0800.0620.1140.1570.0720.0730.0290.075
AmountOfVehicleDamage0.0510.0040.004-0.0130.0480.0030.001-0.0300.2140.9650.6800.7001.0000.0390.0050.0370.0740.0680.0770.0490.0370.5700.0700.4210.3820.0620.0460.2500.0840.0650.0540.0740.0610.1120.1710.0700.0710.0340.066
VehicleAge0.0010.011-0.0250.0640.002-0.0270.016-0.006-0.0430.0280.025-0.0180.0391.0000.0140.0720.0520.0610.0640.0650.0500.0650.0200.0490.0520.0500.0490.0530.0190.0700.0650.0810.0560.0940.0430.0600.0550.0330.090
TimeBetweenCoverageAndIncident0.0460.005-0.0420.0480.0460.0590.001-0.052-0.0490.0150.0050.0130.0050.0141.0000.0660.0480.0550.0680.0550.0600.0650.0350.0460.0550.0450.0480.0740.0840.0610.0870.0380.0540.0970.0410.0400.0400.0280.063
InsuredGender0.0880.6500.0320.0730.0820.0360.0910.0270.0250.0630.0430.0520.0370.0720.0661.0000.0130.0610.0890.0030.0140.0360.0130.0200.0770.0720.0730.0370.0150.0090.0410.0000.0560.1330.0250.0620.0630.0000.033
InsuredEducationLevel0.0530.6040.0650.0620.0520.0300.0520.0320.0460.0730.0600.0650.0740.0520.0480.0131.0000.0770.0950.0530.0540.0790.0870.0720.0560.0620.0600.0460.0920.0670.0580.0900.0810.1300.0370.0830.0910.0090.058
InsuredOccupation0.0550.5750.0660.0730.0590.0520.0550.0430.0550.0690.0630.0650.0680.0610.0550.0610.0771.0000.0910.0790.0800.1150.0720.0910.0710.0810.0720.0920.0720.1170.0780.0850.0760.1280.1200.1030.1040.0280.097
InsuredHobbies0.0760.5720.0880.0810.0740.0460.0700.0600.0660.0800.0830.0840.0770.0640.0680.0890.0950.0911.0000.0960.0980.1270.1260.1040.1070.1040.0970.0850.1140.1000.0930.1530.0920.1250.3700.1130.1120.0270.080
InsurancePolicyState0.0450.6380.0600.0590.0390.0220.0490.0420.0490.0500.0580.0510.0490.0650.0550.0030.0530.0790.0961.0000.0280.0280.0560.0260.0540.0610.0520.0320.0680.0550.0250.0550.0860.1380.0370.0340.0330.0000.027
InsuredRelationship0.0500.5940.0570.0620.0490.0270.0370.0610.0430.0470.0600.0420.0370.0500.0600.0140.0540.0800.0980.0281.0000.0650.0660.0460.0550.0560.0570.0640.0170.0420.0500.0830.0790.1320.0760.0600.0620.0140.036
TypeOfIncident0.0590.8320.0770.0800.0570.0480.0530.0490.2470.5690.5360.5450.5700.0650.0650.0360.0790.1150.1270.0280.0651.0000.0630.4150.4380.0850.0770.5220.0690.0480.0430.0600.1060.1980.1520.0580.0610.0230.082
TypeOfCollission0.0710.6580.0800.0850.0640.0420.0400.0390.0640.0680.0430.0560.0700.0200.0350.0130.0870.0720.1260.0560.0660.0631.0000.0060.0560.0900.0710.0760.0090.0080.0680.0580.0880.1620.0480.0430.0370.0210.049
SeverityOfIncident0.0650.7270.0640.0720.0620.0300.0560.0420.1830.4200.3940.4010.4210.0490.0460.0200.0720.0910.1040.0260.0460.4150.0061.0000.3200.0660.0500.1770.0780.0090.0440.0690.0880.1560.4470.0430.0380.0210.028
AuthoritiesContacted0.0580.6930.0610.0730.0580.0310.0500.0350.1600.3830.3660.3660.3820.0520.0550.0770.0560.0710.1070.0540.0550.4380.0560.3201.0000.0690.0620.1780.0250.0450.0490.0770.0810.1360.1400.0740.0740.0260.058
IncidentState0.0400.5950.0580.0610.0380.0390.0480.0470.0620.0680.0610.0660.0620.0500.0450.0720.0620.0810.1040.0610.0560.0850.0900.0660.0691.0000.0510.0540.0610.0560.0600.0940.0750.1310.1290.0590.0560.0180.053
IncidentCity0.0490.5960.0480.0600.0480.0360.0590.0510.0500.0530.0500.0570.0460.0490.0480.0730.0600.0720.0970.0520.0570.0770.0710.0500.0620.0511.0000.0640.1090.0640.0480.0550.0730.1330.0560.0450.0420.0180.038
NumberOfVehicles0.0620.6460.0690.0590.0550.0620.0490.0530.1360.2480.2250.2330.2500.0530.0740.0370.0460.0920.0850.0320.0640.5220.0760.1770.1780.0540.0641.0000.0430.0460.0390.0180.0890.1630.0590.0360.0400.0280.089
PropertyDamage0.0650.7000.0680.1200.0700.0470.0780.0820.0720.0740.0780.0660.0840.0190.0840.0150.0920.0720.1140.0680.0170.0690.0090.0780.0250.0610.1090.0431.0000.0530.0480.0480.0970.1790.0470.0190.0220.0320.036
BodilyInjuries0.0580.6750.0860.0670.0650.0470.0490.0870.0660.0710.0680.0720.0650.0700.0610.0090.0670.1170.1000.0550.0420.0480.0080.0090.0450.0560.0640.0460.0531.0000.0570.0000.0700.1150.0250.0400.0350.0210.035
Witnesses0.0710.6040.0630.0570.0660.0590.0560.0290.0630.0580.0640.0700.0540.0650.0870.0410.0580.0780.0930.0250.0500.0430.0680.0440.0490.0600.0480.0390.0480.0571.0000.0750.0650.1160.0750.0730.0720.0320.044
PoliceReport0.0430.7060.0550.0560.0700.0460.0560.0630.0820.0690.0810.0800.0740.0810.0380.0000.0900.0850.1530.0550.0830.0600.0580.0690.0770.0940.0550.0180.0480.0000.0751.0000.0720.1720.0000.0440.0450.0190.039
VehicleMake0.0590.5970.0710.0700.0590.0410.0560.0580.0550.0680.0650.0620.0610.0560.0540.0560.0810.0760.0920.0860.0790.1060.0880.0880.0810.0750.0730.0890.0970.0700.0650.0721.0000.5710.1090.0910.0910.0230.083
VehicleModel0.0960.5800.1210.1170.0950.0780.0940.0840.0860.1200.1090.1140.1120.0940.0970.1330.1300.1280.1250.1380.1320.1980.1620.1560.1360.1310.1330.1630.1790.1150.1160.1720.5711.0000.1930.1540.1530.0420.152
ReportedFraud0.0280.8360.0770.0310.0420.0470.0580.0810.0490.1650.1370.1570.1710.0430.0410.0250.0370.1200.3700.0370.0760.1520.0480.4470.1400.1290.0560.0590.0470.0250.0750.0000.1090.1931.0000.0340.0380.0210.032
SplitLimit0.0390.7210.0560.0600.0450.0380.0680.0390.0410.0670.0690.0720.0700.0600.0400.0620.0830.1030.1130.0340.0600.0580.0430.0430.0740.0590.0450.0360.0190.0400.0730.0440.0910.1540.0341.0000.7120.0130.028
CombinedSingleLimit0.0390.7190.0630.0570.0490.0430.0630.0290.0390.0650.0660.0730.0710.0550.0400.0630.0910.1040.1120.0330.0620.0610.0370.0380.0740.0560.0420.0400.0220.0350.0720.0450.0910.1530.0380.7121.0000.0200.031
DayOfWeek0.0300.2050.0170.0170.0350.0200.0340.0240.0240.0320.0330.0290.0340.0330.0280.0000.0090.0280.0270.0000.0140.0230.0210.0210.0260.0180.0180.0280.0320.0210.0320.0190.0230.0420.0210.0130.0201.0000.082
MonthOfIncident0.0640.6770.0670.0700.0670.0400.0570.0420.0680.0590.0970.0750.0660.0900.0630.0330.0580.0970.0800.0270.0360.0820.0490.0280.0580.0530.0380.0890.0360.0350.0440.0390.0830.1520.0320.0280.0310.0821.000

Missing values

2023-03-16T06:17:32.480081image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-16T06:17:33.282757image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-03-16T06:17:33.826192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

InsuredAgeInsuredZipCodeInsuredGenderInsuredEducationLevelInsuredOccupationInsuredHobbiesCapitalGainsCapitalLossCustomerLoyaltyPeriodInsurancePolicyStatePolicy_DeductiblePolicyAnnualPremiumUmbrellaLimitInsuredRelationshipTypeOfIncidentTypeOfCollissionSeverityOfIncidentAuthoritiesContactedIncidentStateIncidentCityIncidentAddressIncidentTimeNumberOfVehiclesPropertyDamageBodilyInjuriesWitnessesPoliceReportAmountOfTotalClaimAmountOfInjuryClaimAmountOfPropertyClaimAmountOfVehicleDamageVehicleMakeVehicleModelReportedFraudSplitLimitCombinedSingleLimitVehicleAgeDayOfWeekMonthOfIncidentTimeBetweenCoverageAndIncident
035454776MALEJDarmed-forcesmovies56700-4850049State110001632.730not-in-familyMulti-vehicle CollisionSide CollisionTotal LossPoliceState7City1Location 131117.03NaN10.0NaN65501.013417607146013AudiA5N1003002590TuesdayFebruary5945
136454776MALEJDtech-supportcross-fit70600-48500114State110001255.190not-in-familyMulti-vehicle CollisionSide CollisionTotal LossPoliceState7City5Location 131110.03YES21.0YES61382.015560591939903AudiA5N1003003319MondayFebruary5192
233603260MALEJDarmed-forcespolo66400-63700167State36171373.380wifeSingle Vehicle CollisionSide CollisionMinor DamageOtherState8City6Location 208122.01YES23.0NO66755.0116301163043495VolkswagenJettaN50010005858ThursdayJanuary5085
336474848MALEJDarmed-forcespolo47900-73400190State27221337.600own-childSingle Vehicle CollisionSide CollisionMinor DamageOtherState9City6Location 208122.01YES23.0NO66243.0120031200342237VolkswagenJettaN50010004401MondayJanuary3570
429457942FEMALEHigh Schoolexec-managerialdancing0-41500115State25001353.734279863unmarriedSingle Vehicle CollisionRear CollisionMinor DamageFireState8City6Location 169510.01NO21.0YES53544.08829723437481ToyotaCRVN1003001834FridayJanuary6650
528457942FEMALEHigh Schoolexec-managerialdancing0-41500101State25001334.493921366unmarriedSingle Vehicle CollisionRear CollisionMinor DamageFireState7City6Location 16957.01NO12.0NaN53167.07818813237217ToyotaCRVN1003001498SaturdayFebruary5585
657476456MALEMastersadm-clericalsleeping674000471State35121214.78165819own-childSingle Vehicle CollisionFront CollisionMinor DamageAmbulanceState5City4Location 144020.01NaN02.0NO77453.064761282258155MercedesC300N1003005508FridayJanuary7286
749476456MALEMastersadm-clericalsleeping674000340State38771159.815282219own-childSingle Vehicle CollisionFront CollisionMinor DamagePoliceState5City3Location 144018.01NaN02.0NO60569.05738733347498SuburuC300N1003001837MondayJanuary7994
827432896FEMALEHigh Schoolhandlers-cleanerscamping56400-3280081State22000989.530own-childMulti-vehicle CollisionFront CollisionMinor DamageAmbulanceState9City2Location 15213.03YES00.0NaN67876.06788750453584VolkswagenPassatN50010007341FridayFebruary6117
948466132MALEMDcraft-repairsleeping533000328State310001406.910husbandSingle Vehicle CollisionSide CollisionMajor DamagePoliceState7City2Location 15965.01YES12.0YES71610.065101302052080Saab92xY2505004042SundayJanuary100
InsuredAgeInsuredZipCodeInsuredGenderInsuredEducationLevelInsuredOccupationInsuredHobbiesCapitalGainsCapitalLossCustomerLoyaltyPeriodInsurancePolicyStatePolicy_DeductiblePolicyAnnualPremiumUmbrellaLimitInsuredRelationshipTypeOfIncidentTypeOfCollissionSeverityOfIncidentAuthoritiesContactedIncidentStateIncidentCityIncidentAddressIncidentTimeNumberOfVehiclesPropertyDamageBodilyInjuriesWitnessesPoliceReportAmountOfTotalClaimAmountOfInjuryClaimAmountOfPropertyClaimAmountOfVehicleDamageVehicleMakeVehicleModelReportedFraudSplitLimitCombinedSingleLimitVehicleAgeDayOfWeekMonthOfIncidentTimeBetweenCoverageAndIncident
2882631614187FEMALEHigh Schoolpriv-house-servmovies39600-6430099State2831978.060husbandSingle Vehicle CollisionFront CollisionTotal LossOtherState5City3Location 103514.01NaN22.0YES85436.0131311313159174SaabNeonN2505005860SaturdayJanuary5936
2882738434293MALEMDpriv-house-servexercise46300-49200239State218581125.11568851unmarriedMulti-vehicle CollisionFront CollisionTotal LossFireState5City4Location 19083.03NaN13.0NaN51953.05385538541183Saab93N50010006227MondayJanuary8636
2882849468313MALEMDpriv-house-servexercise463000339State213441129.522623811unmarriedMulti-vehicle CollisionFront CollisionTotal LossOtherState5City4Location 190814.03NaN23.0NaN57635.08293829341049Suburu93N50010004029MondayJanuary6680
2882945450339FEMALEAssociatecraft-repairdancing00296State39861596.500wifeSingle Vehicle CollisionRear CollisionMinor DamageOtherState5City2Location 16588.01YES12.0NO63845.012354825243239BMWCivicN1003006258ThursdayFebruary4726
2883045600561MALEMastersprotective-servdancing00296State19521347.420other-relativeVehicle TheftNaNTrivial DamageNoneState9City4Location 18584.01NO02.0NaN7262.069813045260FordImprezaN50010006219SundayJanuary8374
2883146600561MALEMastersprotective-servsleeping00321State16551276.010unmarriedVehicle TheftNaNTrivial DamagePoliceState9City4Location 18903.01NO03.0NaN6590.087010784642SuburuImprezaN50010002945SaturdayJanuary6125
2883244439304MALEMastersadm-clericaldancing0-28800237State210891273.380unmarriedSingle Vehicle CollisionFront CollisionMinor DamageFireState8City3Location 209717.01YES02.0YES74547.014699787551973JeepWranglerN1003004422MondayFebruary3807
2883353460722MALEPhDtransport-movingbase-jumping63100-43800392State37871380.923448735own-childSingle Vehicle CollisionRear CollisionMinor DamageFireState9City7Location 14521.01YES20.0YES55305.07043704341219SuburuLegacyN2505004045WednesdayJanuary5984
2883453472634MALEAssociatetransport-movingskydiving67400-43800391State37801389.293364301not-in-familySingle Vehicle CollisionRear CollisionMinor DamageAmbulanceState9City3Location 18761.01NaN20.0YES55830.07059705941712SuburuForrestorN2505004045WednesdayJanuary6015
2883536450730FEMALEPhDhandlers-cleanersbase-jumping46400-74300191State22000928.432909175husbandSingle Vehicle CollisionFront CollisionTotal LossOtherState8City3Location 18749.01NO13.0YES68969.012075603850856SuburuE400N50010002934TuesdayJanuary4294